class_docstr: Detection Head of `DDOD <https://arxiv.org/abs/2107.02963>`_.
  
  DDOD head decomposes conjunctions lying in most current one-stage
  detectors via label assignment disentanglement, spatial feature
  disentanglement, and pyramid supervision disentanglement.
  
  Args:
      num_classes (int): Number of categories excluding the
          background category.
      in_channels (int): Number of channels in the input feature map.
      stacked_convs (int): The number of stacked Conv. Defaults to 4.
      conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
          convolution layer. Defaults to None.
      use_dcn (bool): Use dcn, Same as ATSS when False. Defaults to True.
      norm_cfg (:obj:`ConfigDict` or dict): Normal config of ddod head.
          Defaults to dict(type='GN', num_groups=32, requires_grad=True).
      loss_iou (:obj:`ConfigDict` or dict): Config of IoU loss. Defaults to
          dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0).
docstr: Initialize weights of the head.
docstr_tok: ["Initialize","weights","of","the","head","."]
code: def init_weights(self) -> None:
      """Initialize weights of the head."""
      for m in self.cls_convs:
          normal_init(m.conv, std=0.01)
      for m in self.reg_convs:
          normal_init(m.conv, std=0.01)
      normal_init(self.atss_reg, std=0.01)
      normal_init(self.atss_iou, std=0.01)
      bias_cls = bias_init_with_prob(0.01)
      normal_init(self.atss_cls, std=0.01, bias=bias_cls)
  
imports: from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmengine.model import bias_init_with_prob, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import bbox_overlaps
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import anchor_inside_flags
  from ..utils import images_to_levels, multi_apply, unmap
  from .anchor_head import AnchorHead
label_desc: Use image node_id 3 for calling the DDODHead obj's underlying member method code with example usage: obj.init_weights() without return types